Overview

Dataset statistics

Number of variables32
Number of observations1867
Missing cells109
Missing cells (%)0.2%
Duplicate rows6
Duplicate rows (%)0.3%
Total size in memory466.9 KiB
Average record size in memory256.1 B

Variable types

NUM17
CAT14
UNSUPPORTED1

Warnings

Dataset has 6 (0.3%) duplicate rows Duplicates
Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
Fstf has 109 (5.8%) missing values Missing
WoTag is an unsupported type, check if it needs cleaning or further analysis Unsupported
TempDist has 858 (46.0%) zeros Zeros
SpatDist has 1584 (84.8%) zeros Zeros
UArt1 has 64 (3.4%) zeros Zeros
AUrs1 has 1676 (89.8%) zeros Zeros
AUrs2 has 1856 (99.4%) zeros Zeros

Reproduction

Analysis started2020-10-28 07:46:04.173082
Analysis finished2020-10-28 07:47:03.560388
Duration59.39 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

TempMax
Real number (ℝ≥0)

Distinct211
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.0091055
Minimum9
Maximum1341
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:04.018290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24
Q169
median117
Q3216
95-th percentile525
Maximum1341
Range1332
Interquartile range (IQR)147

Descriptive statistics

Standard deviation173.2912269
Coefficient of variation (CV)0.9901840616
Kurtosis9.396857376
Mean175.0091055
Median Absolute Deviation (MAD)63
Skewness2.581777376
Sum326742
Variance30029.84933
MonotocityNot monotonic
2020-10-28T08:47:04.203809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
84361.9%
 
81361.9%
 
93351.9%
 
87331.8%
 
72311.7%
 
111311.7%
 
78311.7%
 
54311.7%
 
69311.7%
 
96301.6%
 
Other values (201)154282.6%
 
ValueCountFrequency (%) 
980.4%
 
12110.6%
 
15130.7%
 
18281.5%
 
21201.1%
 
ValueCountFrequency (%) 
134110.1%
 
132330.2%
 
125720.1%
 
119410.1%
 
115210.1%
 

TempAvg
Real number (ℝ≥0)

Distinct246
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.23245849
Minimum3
Maximum1326
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:04.372010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q132
median55
Q390
95-th percentile196
Maximum1326
Range1323
Interquartile range (IQR)58

Descriptive statistics

Standard deviation81.24420703
Coefficient of variation (CV)1.094456639
Kurtosis72.03526142
Mean74.23245849
Median Absolute Deviation (MAD)28
Skewness6.318025592
Sum138592
Variance6600.621175
MonotocityNot monotonic
2020-10-28T08:47:04.541010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
44301.6%
 
26271.4%
 
48271.4%
 
53261.4%
 
59261.4%
 
24251.3%
 
56241.3%
 
57231.2%
 
50231.2%
 
47231.2%
 
Other values (236)161386.4%
 
ValueCountFrequency (%) 
310.1%
 
430.2%
 
5110.6%
 
6130.7%
 
7181.0%
 
ValueCountFrequency (%) 
132610.1%
 
126010.1%
 
95510.1%
 
92010.1%
 
70310.1%
 

SpatMax
Real number (ℝ≥0)

Distinct1517
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12099.28388
Minimum832
Maximum219082
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:04.714882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum832
5-th percentile1899.3
Q14518.5
median8422
Q314539
95-th percentile31525.9
Maximum219082
Range218250
Interquartile range (IQR)10020.5

Descriptive statistics

Standard deviation17042.85808
Coefficient of variation (CV)1.408584033
Kurtosis82.68306322
Mean12099.28388
Median Absolute Deviation (MAD)4623
Skewness7.967522598
Sum22589363
Variance290459011.6
MonotocityNot monotonic
2020-10-28T08:47:04.902662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1316380.4%
 
18973050.3%
 
662150.3%
 
235150.3%
 
347550.3%
 
1511740.2%
 
484040.2%
 
3549240.2%
 
1345640.2%
 
602540.2%
 
Other values (1507)181997.4%
 
ValueCountFrequency (%) 
83220.1%
 
96510.1%
 
97110.1%
 
100010.1%
 
103610.1%
 
ValueCountFrequency (%) 
21908230.2%
 
19531020.1%
 
18973050.3%
 
15323710.1%
 
13578010.1%
 

SpatAvg
Real number (ℝ≥0)

Distinct1498
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3996.387788
Minimum135
Maximum17805
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:05.081157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1053.3
Q12007.5
median3402
Q35334.5
95-th percentile9279
Maximum17805
Range17670
Interquartile range (IQR)3327

Descriptive statistics

Standard deviation2621.118875
Coefficient of variation (CV)0.655872006
Kurtosis2.208051196
Mean3996.387788
Median Absolute Deviation (MAD)1569
Skewness1.329804557
Sum7461256
Variance6870264.157
MonotocityNot monotonic
2020-10-28T08:47:05.250582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1026650.3%
 
141350.3%
 
560650.3%
 
882240.2%
 
1236340.2%
 
442340.2%
 
752730.2%
 
147530.2%
 
641530.2%
 
793330.2%
 
Other values (1488)182897.9%
 
ValueCountFrequency (%) 
13510.1%
 
34710.1%
 
35810.1%
 
38710.1%
 
39310.1%
 
ValueCountFrequency (%) 
1780510.1%
 
1685110.1%
 
1657120.1%
 
1552610.1%
 
1513210.1%
 

TempDist
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.538832351
Minimum0
Maximum24
Zeros858
Zeros (%)46.0%
Memory size14.6 KiB
2020-10-28T08:47:05.406954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q39
95-th percentile21
Maximum24
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.888638558
Coefficient of variation (CV)1.243698693
Kurtosis0.1675782571
Mean5.538832351
Median Absolute Deviation (MAD)3
Skewness1.112474575
Sum10341
Variance47.45334119
MonotocityNot monotonic
2020-10-28T08:47:05.707340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
085846.0%
 
6894.8%
 
7764.1%
 
5703.7%
 
8683.6%
 
9663.5%
 
10603.2%
 
3583.1%
 
12512.7%
 
4502.7%
 
Other values (15)42122.5%
 
ValueCountFrequency (%) 
085846.0%
 
1412.2%
 
2321.7%
 
3583.1%
 
4502.7%
 
ValueCountFrequency (%) 
24281.5%
 
23211.1%
 
22271.4%
 
21311.7%
 
20191.0%
 

SpatDist
Real number (ℝ≥0)

ZEROS

Distinct220
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.7728977
Minimum0
Maximum2000
Zeros1584
Zeros (%)84.8%
Memory size14.6 KiB
2020-10-28T08:47:05.853803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile650
Maximum2000
Range2000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation280.5631239
Coefficient of variation (CV)3.309585157
Kurtosis18.59098594
Mean84.7728977
Median Absolute Deviation (MAD)0
Skewness4.169201904
Sum158271
Variance78715.66651
MonotocityNot monotonic
2020-10-28T08:47:06.026948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0158484.8%
 
250150.8%
 
75080.4%
 
125060.3%
 
5030.2%
 
15130.2%
 
29030.2%
 
17030.2%
 
46820.1%
 
34120.1%
 
Other values (210)23812.7%
 
ValueCountFrequency (%) 
0158484.8%
 
210.1%
 
320.1%
 
710.1%
 
1310.1%
 
ValueCountFrequency (%) 
200020.1%
 
197510.1%
 
196010.1%
 
194910.1%
 
190610.1%
 

Coverage
Real number (ℝ≥0)

Distinct96
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.58543117
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:06.215113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13
Q127
median41
Q358
95-th percentile85
Maximum100
Range98
Interquartile range (IQR)31

Descriptive statistics

Standard deviation21.43115206
Coefficient of variation (CV)0.4917044866
Kurtosis-0.363760615
Mean43.58543117
Median Absolute Deviation (MAD)15
Skewness0.4941271419
Sum81374
Variance459.2942785
MonotocityNot monotonic
2020-10-28T08:47:06.388378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
42452.4%
 
30422.2%
 
36422.2%
 
44412.2%
 
25402.1%
 
40402.1%
 
31392.1%
 
37382.0%
 
18372.0%
 
45361.9%
 
Other values (86)146778.6%
 
ValueCountFrequency (%) 
230.2%
 
350.3%
 
560.3%
 
660.3%
 
760.3%
 
ValueCountFrequency (%) 
100181.0%
 
9830.2%
 
9720.1%
 
9640.2%
 
9530.2%
 

TLCar
Real number (ℝ≥0)

Distinct828
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1510.77022
Minimum1000
Maximum1999
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:06.561324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1052
Q11263
median1518
Q31764
95-th percentile1948
Maximum1999
Range999
Interquartile range (IQR)501

Descriptive statistics

Standard deviation288.4265018
Coefficient of variation (CV)0.1909135473
Kurtosis-1.208499076
Mean1510.77022
Median Absolute Deviation (MAD)250
Skewness-0.03924223406
Sum2820608
Variance83189.84696
MonotocityNot monotonic
2020-10-28T08:47:06.728678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
117170.4%
 
129370.4%
 
119170.4%
 
195570.4%
 
190270.4%
 
199960.3%
 
140360.3%
 
147260.3%
 
186660.3%
 
167860.3%
 
Other values (818)180296.5%
 
ValueCountFrequency (%) 
100010.1%
 
100150.3%
 
100220.1%
 
100330.2%
 
100630.2%
 
ValueCountFrequency (%) 
199960.3%
 
199830.2%
 
199720.1%
 
199630.2%
 
199420.1%
 

TLHGV
Real number (ℝ≥0)

Distinct488
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.8339582
Minimum500
Maximum999
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:06.887750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile525.3
Q1620
median743
Q3871
95-th percentile972
Maximum999
Range499
Interquartile range (IQR)251

Descriptive statistics

Standard deviation144.8114466
Coefficient of variation (CV)0.193900458
Kurtosis-1.233212315
Mean746.8339582
Median Absolute Deviation (MAD)126
Skewness0.03726417494
Sum1394339
Variance20970.35505
MonotocityNot monotonic
2020-10-28T08:47:07.055059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
926110.6%
 
582100.5%
 
871100.5%
 
869100.5%
 
62690.5%
 
79590.5%
 
56790.5%
 
98290.5%
 
57990.5%
 
66490.5%
 
Other values (478)177294.9%
 
ValueCountFrequency (%) 
50030.2%
 
50170.4%
 
50230.2%
 
50310.1%
 
50410.1%
 
ValueCountFrequency (%) 
99950.3%
 
99840.2%
 
99710.1%
 
99630.2%
 
99530.2%
 

Strasse
Categorical

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
A3
574 
A9
466 
A96
156 
A7
130 
A73
129 
Other values (12)
412 
ValueCountFrequency (%) 
A357430.7%
 
A946625.0%
 
A961568.4%
 
A71307.0%
 
A731296.9%
 
A61276.8%
 
A991166.2%
 
A92663.5%
 
A94372.0%
 
A70311.7%
 
Other values (7)351.9%
 
2020-10-28T08:47:07.222202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-10-28T08:47:07.372560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.30690948
Min length2

Kat
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
3
890 
7
724 
2
217 
1
 
36
ValueCountFrequency (%) 
389047.7%
 
772438.8%
 
221711.6%
 
1361.9%
 
2020-10-28T08:47:07.523485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:07.616895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:07.745628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Real number (ℝ≥0)

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.054097483
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:07.857004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.94780996
Coefficient of variation (CV)0.385392242
Kurtosis0.214578889
Mean5.054097483
Median Absolute Deviation (MAD)0
Skewness-1.388058215
Sum9436
Variance3.793963641
MonotocityNot monotonic
2020-10-28T08:47:07.960614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
6131370.3%
 
130116.1%
 
31206.4%
 
71186.3%
 
5110.6%
 
440.2%
 
ValueCountFrequency (%) 
130116.1%
 
31206.4%
 
440.2%
 
5110.6%
 
6131370.3%
 
ValueCountFrequency (%) 
71186.3%
 
6131370.3%
 
5110.6%
 
440.2%
 
31206.4%
 

Betei
Real number (ℝ≥0)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.275307981
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:08.073953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9596611188
Coefficient of variation (CV)0.4217719654
Kurtosis42.98696399
Mean2.275307981
Median Absolute Deviation (MAD)0
Skewness3.808235232
Sum4248
Variance0.920949463
MonotocityNot monotonic
2020-10-28T08:47:08.181981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
2114561.3%
 
335819.2%
 
121911.7%
 
41035.5%
 
5251.3%
 
760.3%
 
660.3%
 
840.2%
 
1810.1%
 
ValueCountFrequency (%) 
121911.7%
 
2114561.3%
 
335819.2%
 
41035.5%
 
5251.3%
 
ValueCountFrequency (%) 
1810.1%
 
840.2%
 
760.3%
 
660.3%
 
5251.3%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.38189609
Minimum0
Maximum9
Zeros64
Zeros (%)3.4%
Memory size14.6 KiB
2020-10-28T08:47:08.306298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.438164325
Coefficient of variation (CV)0.7209459606
Kurtosis0.3529952481
Mean3.38189609
Median Absolute Deviation (MAD)1
Skewness1.283502637
Sum6314
Variance5.944645278
MonotocityNot monotonic
2020-10-28T08:47:08.413138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
283844.9%
 
345324.3%
 
81658.8%
 
91256.7%
 
5904.8%
 
1874.7%
 
0643.4%
 
7351.9%
 
660.3%
 
440.2%
 
ValueCountFrequency (%) 
0643.4%
 
1874.7%
 
283844.9%
 
345324.3%
 
440.2%
 
ValueCountFrequency (%) 
91256.7%
 
81658.8%
 
7351.9%
 
660.3%
 
5904.8%
 

UArt2
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1815747188
Minimum-1
Maximum9
Zeros4
Zeros (%)0.2%
Memory size14.6 KiB
2020-10-28T08:47:08.527619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.052896133
Coefficient of variation (CV)16.81344271
Kurtosis3.665719041
Mean0.1815747188
Median Absolute Deviation (MAD)0
Skewness2.338769952
Sum339
Variance9.320174797
MonotocityNot monotonic
2020-10-28T08:47:08.639649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1159885.6%
 
91337.1%
 
8693.7%
 
3392.1%
 
2120.6%
 
150.3%
 
740.2%
 
040.2%
 
520.1%
 
410.1%
 
ValueCountFrequency (%) 
-1159885.6%
 
040.2%
 
150.3%
 
2120.6%
 
3392.1%
 
ValueCountFrequency (%) 
91337.1%
 
8693.7%
 
740.2%
 
520.1%
 
410.1%
 

AUrs1
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.79914301
Minimum0
Maximum89
Zeros1676
Zeros (%)89.8%
Memory size14.6 KiB
2020-10-28T08:47:08.905344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.18858423
Coefficient of variation (CV)2.973222084
Kurtosis5.183920285
Mean7.79914301
Median Absolute Deviation (MAD)0
Skewness2.662744052
Sum14561
Variance537.7104387
MonotocityNot monotonic
2020-10-28T08:47:09.020611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
0167689.8%
 
73955.1%
 
72412.2%
 
89181.0%
 
82130.7%
 
8880.4%
 
8140.2%
 
8630.2%
 
8320.1%
 
7520.1%
 
Other values (5)50.3%
 
ValueCountFrequency (%) 
0167689.8%
 
72412.2%
 
73955.1%
 
7520.1%
 
7610.1%
 
ValueCountFrequency (%) 
89181.0%
 
8880.4%
 
8710.1%
 
8630.2%
 
8410.1%
 

AUrs2
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4627745046
Minimum0
Maximum89
Zeros1856
Zeros (%)99.4%
Memory size14.6 KiB
2020-10-28T08:47:09.135363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.024964269
Coefficient of variation (CV)13.01922256
Kurtosis167.9982767
Mean0.4627745046
Median Absolute Deviation (MAD)0
Skewness13.00346141
Sum864
Variance36.30019444
MonotocityNot monotonic
2020-10-28T08:47:09.235220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
0185699.4%
 
7330.2%
 
8120.1%
 
8020.1%
 
7520.1%
 
8910.1%
 
8410.1%
 
ValueCountFrequency (%) 
0185699.4%
 
7330.2%
 
7520.1%
 
8020.1%
 
8120.1%
 
ValueCountFrequency (%) 
8910.1%
 
8410.1%
 
8120.1%
 
8020.1%
 
7520.1%
 

AufHi
Real number (ℝ)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01446170327
Minimum-1
Maximum9
Zeros2
Zeros (%)0.1%
Memory size14.6 KiB
2020-10-28T08:47:09.341938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile3
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.838700543
Coefficient of variation (CV)127.1427375
Kurtosis0.6646081812
Mean0.01446170327
Median Absolute Deviation (MAD)0
Skewness1.416672657
Sum27
Variance3.380819686
MonotocityNot monotonic
2020-10-28T08:47:09.449917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
-1141775.9%
 
338120.4%
 
4442.4%
 
5160.9%
 
830.2%
 
920.1%
 
020.1%
 
210.1%
 
110.1%
 
ValueCountFrequency (%) 
-1141775.9%
 
020.1%
 
110.1%
 
210.1%
 
338120.4%
 
ValueCountFrequency (%) 
920.1%
 
830.2%
 
5160.9%
 
4442.4%
 
338120.4%
 

Alkoh
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
-1
1842 
1
 
25
ValueCountFrequency (%) 
-1184298.7%
 
1251.3%
 
2020-10-28T08:47:09.578727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:09.667228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:09.774290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.986609534
Min length1

Char1
Real number (ℝ)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5206213176
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size14.6 KiB
2020-10-28T08:47:09.889113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile4
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.607184446
Coefficient of variation (CV)-3.087050782
Kurtosis8.211005847
Mean-0.5206213176
Median Absolute Deviation (MAD)0
Skewness3.147680846
Sum-972
Variance2.583041843
MonotocityNot monotonic
2020-10-28T08:47:09.993980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
-1171091.6%
 
5603.2%
 
4563.0%
 
6331.8%
 
280.4%
 
ValueCountFrequency (%) 
-1171091.6%
 
280.4%
 
4563.0%
 
5603.2%
 
6331.8%
 
ValueCountFrequency (%) 
6331.8%
 
5603.2%
 
4563.0%
 
280.4%
 
-1171091.6%
 

Char2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
-1
1825 
6
 
42
ValueCountFrequency (%) 
-1182597.8%
 
6422.2%
 
2020-10-28T08:47:10.130571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:10.217769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:10.323486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.977504017
Min length1

Bes1
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
-1
1503 
6
358 
1
 
6
ValueCountFrequency (%) 
-1150380.5%
 
635819.2%
 
160.3%
 
2020-10-28T08:47:10.462543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:10.558191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:10.678946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.805034815
Min length1

Bes2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
-1
1866 
6
 
1
ValueCountFrequency (%) 
-1186699.9%
 
610.1%
 
2020-10-28T08:47:10.806455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-28T08:47:10.891700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:10.993558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.999464381
Min length1

Lich1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
0
1503 
2
263 
1
 
98
-1
 
3
ValueCountFrequency (%) 
0150380.5%
 
226314.1%
 
1985.2%
 
-130.2%
 
2020-10-28T08:47:11.132207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:11.230613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:11.362545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.001606856
Min length1

Lich2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
-1
1506 
4
345 
3
 
16
ValueCountFrequency (%) 
-1150680.7%
 
434518.5%
 
3160.9%
 
2020-10-28T08:47:11.502027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:11.597561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:11.720070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.806641671
Min length1

Zust1
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
0
1408 
1
414 
2
 
40
-1
 
5
ValueCountFrequency (%) 
0140875.4%
 
141422.2%
 
2402.1%
 
-150.3%
 
2020-10-28T08:47:11.863325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:11.963414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:12.099570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.002678093
Min length1

Zust2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
-1
1850 
2
 
17
ValueCountFrequency (%) 
-1185099.1%
 
2170.9%
 
2020-10-28T08:47:12.235234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:12.323428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:12.426126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.990894483
Min length1

Fstf
Categorical

MISSING

Distinct7
Distinct (%)0.4%
Missing109
Missing (%)5.8%
Memory size14.6 KiB
2
809 
1
587 
3
292 
4
 
39
S
 
23
Other values (2)
 
8
ValueCountFrequency (%) 
280943.3%
 
158731.4%
 
329215.6%
 
4392.1%
 
S231.2%
 
550.3%
 
F30.2%
 
(Missing)1095.8%
 
2020-10-28T08:47:12.721443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:12.832411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:13.019881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.116764863
Min length1

StrklVu
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
A
1862 
G
 
4
B
 
1
ValueCountFrequency (%) 
A186299.7%
 
G40.2%
 
B10.1%
 
2020-10-28T08:47:13.176089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-28T08:47:13.274176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:13.393928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

WoTag
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size14.7 KiB

FeiTag
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
-1
1820 
1
 
47
ValueCountFrequency (%) 
-1182097.5%
 
1472.5%
 
2020-10-28T08:47:13.523499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:13.612906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:13.717610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.974825924
Min length1

Month
Categorical

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
Jul
242 
Aug
222 
Oct
167 
Jun
163 
Sep
163 
Other values (7)
910 
ValueCountFrequency (%) 
Jul24213.0%
 
Aug22211.9%
 
Oct1678.9%
 
Jun1638.7%
 
Sep1638.7%
 
Apr1508.0%
 
Mar1427.6%
 
Nov1417.6%
 
May1407.5%
 
Dec1377.3%
 
Other values (2)20010.7%
 
2020-10-28T08:47:13.871639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-28T08:47:14.021819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-10-28T08:46:10.026461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:11.721716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:11.897372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:12.052326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:12.223337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:12.397249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:12.581403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:12.742852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:13.046977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:13.223018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:13.402928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:14.469440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:14.621104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:14.770493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:14.916392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:15.073190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:15.244878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:15.971122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:16.121471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:16.266563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:16.404210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:16.551289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:16.703534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:16.878301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:17.035634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:17.183102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:17.329293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:17.473313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:17.626943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:17.762688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:17.918966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:18.086412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:18.254835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:18.414231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:18.591954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:18.745199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:18.891354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:19.036659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:19.195984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:19.335386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:19.485640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:19.623581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:19.915708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:20.063863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:20.205412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:20.343059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:20.474326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:20.625351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:20.771558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:20.918301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:21.067223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:21.226875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:21.406857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:21.582555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:21.747582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:21.927355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:22.098535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:22.281935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:22.456234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:22.629719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:22.800820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:22.974554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:23.148705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:23.316331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:23.483307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:23.651290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:23.816662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:23.983782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:24.163091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:24.331700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:24.499698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:24.653947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:24.821743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:24.979375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:25.146610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:25.304656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-28T08:46:58.735574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:58.911805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:59.088167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:59.422185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:59.595644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:59.763202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:46:59.930891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:00.097543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:00.263365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-28T08:47:14.192674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-28T08:47:14.662293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-28T08:47:15.109496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-28T08:47:15.573218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-28T08:47:16.015840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-28T08:47:00.697509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:02.592087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-28T08:47:03.299192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

TempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuWoTagFeiTagMonth
03602226568431100611714718A32132-100-1-1-1-1-1-10-11-12ADi1Jan
1692860003475100411691686A63632-1890-1-1-1-1-1-10-10-12ADi1Jan
21628913925721200501293804A336529003-1-1-1-1-10-11-12AMi-1Jan
3162891392572121996501293804A33672-1820-1-1-1-1-1-10-11-12AMi-1Jan
41624920701584700281417502A33622-100-1-1-1-1-1-10-10-1NaNAMi-1Jan
545207483305700421044780A63632-100-11-1-1-1-10-10-11AMi-1Jan
628511718067506800281205643A97133-1720-1-1-1-1-1-10-1121AMi-1Jan
721696129915615110431647670A33733-100-1-1-1-1-1-1241-12ADo-1Jan
8138556415314200421803985A97123-100-1-1-1-1-1-1240-11AFr-1Jan
910561994125550112221657905A93632-100-1-1-1-1-1-1241-14AFr-1Jan

Last rows

TempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuWoTagFeiTagMonth
185781612698236080861305511A73119-1003-16-1-1-1240-12AFr-1Dec
18582375229229630500211269784A93632-100-1-1-1-1-1-10-10-1FAFr-1Dec
18595649443244704700161785638A731429003-1-1-1-1-10-10-12A-1-1Dec
186020193149994372160291875579A73622-100-1-1-1-1-1-10-10-11ASa-1Dec
1861452639552768220751262925A93622-100-1-1-1-1-1-10-10-11ASo-1Dec
186239311345610007110741850582A93734-100-1-1-1-1-1-1240-13ASo-1Dec
1863135546481278412750421798511A97622-100-1-1-1-16-10-10-11ASo-1Dec
186487793788341100881363741A923622-100-11-1-1-1-1240-12ASo-1Dec
186593723418257140751950993A33632-100-1-1-1-1-1-1230-12A-1-1Dec
186669621406126060781155500A712622-100-1-1-1-1-1-10-10-11ADi-1Dec

Duplicate rows

Most frequent

TempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfStrklVuFeiTagMonthcount
0242413411033183881451951A97117-1880-1-1-1-16-1241-12A-1Oct2
1994313079633200481149610A97110-100-1-1-1-1-1-10-10-1SA-1Jul2
21776130496535000161194546A37325-100-1-1-1-1-1-10-11-12A-1May2
32525120792425900191679606A33622-100-1-1-1-1-1-10-10-12A-1Sep2
4294947660240500301576680A37625-100-1-1-1-16-10-10-12A-1Aug2
54501709574390700401214522A97623-100-1-1-1-16-10-10-12A-1Jun2